PROJECT SUMMARY For the 1.5 million individuals in the United States living with type 1 diabetes (T1D) the importance of maintaining near normal glycemic control, typically measured by glycosylated hemoglobin (A1C), to prevent microvascular and macrovascular complications is well established. Despite this, most individuals do not meet their glycemic targets with preteens and adolescents faring far worse than their older and younger counterparts. Insulin pump therapy can improve glycemic control and quality of life, however, unlike younger and older age groups, adolescents show no significant improvement in A1C with insulin pump therapy. Insulin pump therapy is a more physiologic form of insulin delivery than multiple daily injections yet these complex machines rely heavily on individual proficiency, surveillance and self-management behaviors to achieve clinical benefit. Suboptimal pump management and unsafe pump behaviors can lead to a decline in glycemic control and adverse events. Research examining insulin pump self-management is limited. The insulin pump download is a valuable objective measure of insulin pump adherence and utilization, yet it remains underutilized in diabetes research. Many insulin pumps have incorporated cloud based platforms allowing patient insulin pump data to be directly transferred and stored indefinitely providing a fertile source of diabetes self-management ?big data.? Identification of key factors effecting insulin pump self-management and, hence, glycemic control is essential for the development of innovative approaches to improve insulin pump self- management. In this proposed mixed method study, we will (1) analyze current insulin pump management behaviors (e.g., frequency of blood glucose testing, insulin bolus frequency, use of bolus calculator, use of advanced features) among 80 preteens and adolescents with T1D by downloading participant's personal insulin pump; (2) correlate insulin pump download data with measures of glycemic control (e.g., A1C; time in target 70-180mg/dl); and (3) describe the experience of insulin pump self-management, including facilitators and barriers of insulin pump adherence and use, among a subset of preteens/adolescents with good (n=10) and poor (n=10) glycemic control. Bivariate tests, Multiple Linear Regressions and Poisson regression will be used in our analysis of derived insulin pump self-management variables and glycemic control, adjusting for covariates. Further analyses using big data approaches including data visualization will be explored during this research assistantship. The goals of the pre-doctoral research training plan are: 1) Build expertise of adolescent development and behavior; 2) Develop skills in designing and implementing research focused on pre-teen and adolescent chronic disease self-management; 3) Develop skills in `big data' analysis and 4) Develop skills in grantsmanship and research dissemination. This research training proposal is congruent with NINR's mission to support research that models or improves understanding of self-management behaviors as well as cross-cutting alignment in advancing big data analytics for technology to improve health.